Overview

Dataset statistics

Number of variables50
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.8 MiB
Average record size in memory400.0 B

Variable types

Numeric23
Text7
Categorical7
Boolean13

Alerts

TimeZone is highly imbalanced (54.2%)Imbalance
CaseOrder is uniformly distributedUniform
CaseOrder has unique valuesUnique
Customer_id has unique valuesUnique
Interaction has unique valuesUnique
UID has unique valuesUnique
Population has 109 (1.1%) zerosZeros
Children has 2548 (25.5%) zerosZeros
Full_meals_eaten has 3715 (37.1%) zerosZeros
vitD_supp has 6702 (67.0%) zerosZeros

Reproduction

Analysis started2024-05-06 23:57:14.641092
Analysis finished2024-05-06 23:57:46.648471
Duration32.01 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

CaseOrder
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5000.5
Minimum1
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-06T19:57:46.709474image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile500.95
Q12500.75
median5000.5
Q37500.25
95-th percentile9500.05
Maximum10000
Range9999
Interquartile range (IQR)4999.5

Descriptive statistics

Standard deviation2886.8957
Coefficient of variation (CV)0.5773214
Kurtosis-1.2
Mean5000.5
Median Absolute Deviation (MAD)2500
Skewness0
Sum50005000
Variance8334166.7
MonotonicityStrictly increasing
2024-05-06T19:57:46.790471image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
6671 1
 
< 0.1%
6664 1
 
< 0.1%
6665 1
 
< 0.1%
6666 1
 
< 0.1%
6667 1
 
< 0.1%
6668 1
 
< 0.1%
6669 1
 
< 0.1%
6670 1
 
< 0.1%
6672 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
10000 1
< 0.1%
9999 1
< 0.1%
9998 1
< 0.1%
9997 1
< 0.1%
9996 1
< 0.1%
9995 1
< 0.1%
9994 1
< 0.1%
9993 1
< 0.1%
9992 1
< 0.1%
9991 1
< 0.1%

Customer_id
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
2024-05-06T19:57:46.942488image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.9002
Min length6

Characters and Unicode

Total characters69002
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10000 ?
Unique (%)100.0%

Sample

1st rowC412403
2nd rowZ919181
3rd rowF995323
4th rowA879973
5th rowC544523
ValueCountFrequency (%)
c412403 1
 
< 0.1%
q870521 1
 
< 0.1%
i745784 1
 
< 0.1%
f995323 1
 
< 0.1%
a879973 1
 
< 0.1%
c544523 1
 
< 0.1%
s543885 1
 
< 0.1%
e543302 1
 
< 0.1%
k477307 1
 
< 0.1%
z229385 1
 
< 0.1%
Other values (9990) 9990
99.9%
2024-05-06T19:57:47.162270image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 6101
8.8%
7 6090
8.8%
2 6049
8.8%
1 6010
8.7%
5 5997
8.7%
8 5971
8.7%
6 5961
8.6%
4 5934
8.6%
9 5912
8.6%
0 4977
7.2%
Other values (26) 10000
14.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 69002
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 6101
8.8%
7 6090
8.8%
2 6049
8.8%
1 6010
8.7%
5 5997
8.7%
8 5971
8.7%
6 5961
8.6%
4 5934
8.6%
9 5912
8.6%
0 4977
7.2%
Other values (26) 10000
14.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 69002
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 6101
8.8%
7 6090
8.8%
2 6049
8.8%
1 6010
8.7%
5 5997
8.7%
8 5971
8.7%
6 5961
8.6%
4 5934
8.6%
9 5912
8.6%
0 4977
7.2%
Other values (26) 10000
14.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 69002
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 6101
8.8%
7 6090
8.8%
2 6049
8.8%
1 6010
8.7%
5 5997
8.7%
8 5971
8.7%
6 5961
8.6%
4 5934
8.6%
9 5912
8.6%
0 4977
7.2%
Other values (26) 10000
14.5%

Interaction
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
2024-05-06T19:57:47.281483image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters360000
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10000 ?
Unique (%)100.0%

Sample

1st row8cd49b13-f45a-4b47-a2bd-173ffa932c2f
2nd rowd2450b70-0337-4406-bdbb-bc1037f1734c
3rd rowa2057123-abf5-4a2c-abad-8ffe33512562
4th row1dec528d-eb34-4079-adce-0d7a40e82205
5th row5885f56b-d6da-43a3-8760-83583af94266
ValueCountFrequency (%)
8cd49b13-f45a-4b47-a2bd-173ffa932c2f 1
 
< 0.1%
67b386eb-1d04-4020-9474-542a09d304e3 1
 
< 0.1%
c173d889-8d12-4375-819e-cd83902c9de8 1
 
< 0.1%
a2057123-abf5-4a2c-abad-8ffe33512562 1
 
< 0.1%
1dec528d-eb34-4079-adce-0d7a40e82205 1
 
< 0.1%
5885f56b-d6da-43a3-8760-83583af94266 1
 
< 0.1%
e3b0a319-9e2e-4a23-8752-2fdc736c30f4 1
 
< 0.1%
2fccb53e-bd9a-4eaa-a53c-9dfc0cb83f94 1
 
< 0.1%
ab634508-dd8c-42e5-a4e4-d101a46f2431 1
 
< 0.1%
5acd5dd3-f0ae-41c7-9540-cf3e4ecb2e27 1
 
< 0.1%
Other values (9990) 9990
99.9%
2024-05-06T19:57:47.455051image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 40000
 
11.1%
4 28914
 
8.0%
a 21417
 
5.9%
b 21369
 
5.9%
8 21304
 
5.9%
9 21055
 
5.8%
d 19025
 
5.3%
0 18943
 
5.3%
c 18900
 
5.2%
e 18797
 
5.2%
Other values (7) 130276
36.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 360000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 40000
 
11.1%
4 28914
 
8.0%
a 21417
 
5.9%
b 21369
 
5.9%
8 21304
 
5.9%
9 21055
 
5.8%
d 19025
 
5.3%
0 18943
 
5.3%
c 18900
 
5.2%
e 18797
 
5.2%
Other values (7) 130276
36.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 360000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 40000
 
11.1%
4 28914
 
8.0%
a 21417
 
5.9%
b 21369
 
5.9%
8 21304
 
5.9%
9 21055
 
5.8%
d 19025
 
5.3%
0 18943
 
5.3%
c 18900
 
5.2%
e 18797
 
5.2%
Other values (7) 130276
36.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 360000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 40000
 
11.1%
4 28914
 
8.0%
a 21417
 
5.9%
b 21369
 
5.9%
8 21304
 
5.9%
9 21055
 
5.8%
d 19025
 
5.3%
0 18943
 
5.3%
c 18900
 
5.2%
e 18797
 
5.2%
Other values (7) 130276
36.2%

UID
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
2024-05-06T19:57:47.572541image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters320000
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10000 ?
Unique (%)100.0%

Sample

1st row3a83ddb66e2ae73798bdf1d705dc0932
2nd row176354c5eef714957d486009feabf195
3rd rowe19a0fa00aeda885b8a436757e889bc9
4th rowcd17d7b6d152cb6f23957346d11c3f07
5th rowd2f0425877b10ed6bb381f3e2579424a
ValueCountFrequency (%)
3a83ddb66e2ae73798bdf1d705dc0932 1
 
< 0.1%
e8e016144bfbe14974752d834f530e26 1
 
< 0.1%
a8437eadfb17988b7d7fedcc0468664e 1
 
< 0.1%
e19a0fa00aeda885b8a436757e889bc9 1
 
< 0.1%
cd17d7b6d152cb6f23957346d11c3f07 1
 
< 0.1%
d2f0425877b10ed6bb381f3e2579424a 1
 
< 0.1%
03e447146d4a32e1aaf75727c3d1230c 1
 
< 0.1%
e4884a42ba809df6a89ded6c97f460d4 1
 
< 0.1%
5f78b8699d1aa9b950b562073f629ca2 1
 
< 0.1%
687e7ba1b80022c310fa2d4b00db199a 1
 
< 0.1%
Other values (9990) 9990
99.9%
2024-05-06T19:57:47.759405image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 20220
 
6.3%
f 20208
 
6.3%
7 20196
 
6.3%
c 20127
 
6.3%
b 20115
 
6.3%
3 20110
 
6.3%
a 20101
 
6.3%
5 20016
 
6.3%
8 19950
 
6.2%
e 19935
 
6.2%
Other values (6) 119022
37.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 320000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 20220
 
6.3%
f 20208
 
6.3%
7 20196
 
6.3%
c 20127
 
6.3%
b 20115
 
6.3%
3 20110
 
6.3%
a 20101
 
6.3%
5 20016
 
6.3%
8 19950
 
6.2%
e 19935
 
6.2%
Other values (6) 119022
37.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 320000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 20220
 
6.3%
f 20208
 
6.3%
7 20196
 
6.3%
c 20127
 
6.3%
b 20115
 
6.3%
3 20110
 
6.3%
a 20101
 
6.3%
5 20016
 
6.3%
8 19950
 
6.2%
e 19935
 
6.2%
Other values (6) 119022
37.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 320000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 20220
 
6.3%
f 20208
 
6.3%
7 20196
 
6.3%
c 20127
 
6.3%
b 20115
 
6.3%
3 20110
 
6.3%
a 20101
 
6.3%
5 20016
 
6.3%
8 19950
 
6.2%
e 19935
 
6.2%
Other values (6) 119022
37.2%

City
Text

Distinct6072
Distinct (%)60.7%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
2024-05-06T19:57:47.969900image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length27
Median length24
Mean length8.5533
Min length3

Characters and Unicode

Total characters85533
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4219 ?
Unique (%)42.2%

Sample

1st rowEva
2nd rowMarianna
3rd rowSioux Falls
4th rowNew Richland
5th rowWest Point
ValueCountFrequency (%)
city 210
 
1.7%
new 137
 
1.1%
lake 100
 
0.8%
saint 91
 
0.7%
springs 88
 
0.7%
west 85
 
0.7%
san 84
 
0.7%
beach 69
 
0.5%
north 63
 
0.5%
falls 62
 
0.5%
Other values (5409) 11561
92.1%
2024-05-06T19:57:48.162510image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 8036
 
9.4%
a 7231
 
8.5%
n 6370
 
7.4%
o 6369
 
7.4%
l 6117
 
7.2%
r 5499
 
6.4%
i 5273
 
6.2%
t 4530
 
5.3%
s 3542
 
4.1%
2550
 
3.0%
Other values (42) 30016
35.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 85533
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 8036
 
9.4%
a 7231
 
8.5%
n 6370
 
7.4%
o 6369
 
7.4%
l 6117
 
7.2%
r 5499
 
6.4%
i 5273
 
6.2%
t 4530
 
5.3%
s 3542
 
4.1%
2550
 
3.0%
Other values (42) 30016
35.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 85533
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 8036
 
9.4%
a 7231
 
8.5%
n 6370
 
7.4%
o 6369
 
7.4%
l 6117
 
7.2%
r 5499
 
6.4%
i 5273
 
6.2%
t 4530
 
5.3%
s 3542
 
4.1%
2550
 
3.0%
Other values (42) 30016
35.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 85533
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 8036
 
9.4%
a 7231
 
8.5%
n 6370
 
7.4%
o 6369
 
7.4%
l 6117
 
7.2%
r 5499
 
6.4%
i 5273
 
6.2%
t 4530
 
5.3%
s 3542
 
4.1%
2550
 
3.0%
Other values (42) 30016
35.1%

State
Text

Distinct52
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
2024-05-06T19:57:48.264491image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters20000
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAL
2nd rowFL
3rd rowSD
4th rowMN
5th rowVA
ValueCountFrequency (%)
tx 553
 
5.5%
ca 550
 
5.5%
pa 547
 
5.5%
ny 514
 
5.1%
il 442
 
4.4%
oh 383
 
3.8%
mo 328
 
3.3%
fl 304
 
3.0%
va 287
 
2.9%
ia 276
 
2.8%
Other values (42) 5816
58.2%
2024-05-06T19:57:48.421143image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 2982
14.9%
N 2133
 
10.7%
M 1626
 
8.1%
I 1557
 
7.8%
O 1219
 
6.1%
C 1204
 
6.0%
L 1113
 
5.6%
T 1071
 
5.4%
Y 775
 
3.9%
K 707
 
3.5%
Other values (14) 5613
28.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 2982
14.9%
N 2133
 
10.7%
M 1626
 
8.1%
I 1557
 
7.8%
O 1219
 
6.1%
C 1204
 
6.0%
L 1113
 
5.6%
T 1071
 
5.4%
Y 775
 
3.9%
K 707
 
3.5%
Other values (14) 5613
28.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 2982
14.9%
N 2133
 
10.7%
M 1626
 
8.1%
I 1557
 
7.8%
O 1219
 
6.1%
C 1204
 
6.0%
L 1113
 
5.6%
T 1071
 
5.4%
Y 775
 
3.9%
K 707
 
3.5%
Other values (14) 5613
28.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 2982
14.9%
N 2133
 
10.7%
M 1626
 
8.1%
I 1557
 
7.8%
O 1219
 
6.1%
C 1204
 
6.0%
L 1113
 
5.6%
T 1071
 
5.4%
Y 775
 
3.9%
K 707
 
3.5%
Other values (14) 5613
28.1%

County
Text

Distinct1607
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
2024-05-06T19:57:48.564322image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length21
Median length19
Mean length7.2215
Min length3

Characters and Unicode

Total characters72215
Distinct characters59
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique353 ?
Unique (%)3.5%

Sample

1st rowMorgan
2nd rowJackson
3rd rowMinnehaha
4th rowWaseca
5th rowKing William
ValueCountFrequency (%)
san 140
 
1.3%
st 127
 
1.2%
jefferson 122
 
1.1%
washington 100
 
0.9%
franklin 93
 
0.9%
los 89
 
0.8%
angeles 88
 
0.8%
montgomery 80
 
0.7%
jackson 78
 
0.7%
cook 69
 
0.6%
Other values (1639) 9919
91.0%
2024-05-06T19:57:48.791950image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 7265
 
10.1%
e 7037
 
9.7%
n 5968
 
8.3%
o 5487
 
7.6%
r 4818
 
6.7%
l 3942
 
5.5%
i 3766
 
5.2%
s 3345
 
4.6%
t 3087
 
4.3%
u 1872
 
2.6%
Other values (49) 25628
35.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 72215
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 7265
 
10.1%
e 7037
 
9.7%
n 5968
 
8.3%
o 5487
 
7.6%
r 4818
 
6.7%
l 3942
 
5.5%
i 3766
 
5.2%
s 3345
 
4.6%
t 3087
 
4.3%
u 1872
 
2.6%
Other values (49) 25628
35.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 72215
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 7265
 
10.1%
e 7037
 
9.7%
n 5968
 
8.3%
o 5487
 
7.6%
r 4818
 
6.7%
l 3942
 
5.5%
i 3766
 
5.2%
s 3345
 
4.6%
t 3087
 
4.3%
u 1872
 
2.6%
Other values (49) 25628
35.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 72215
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 7265
 
10.1%
e 7037
 
9.7%
n 5968
 
8.3%
o 5487
 
7.6%
r 4818
 
6.7%
l 3942
 
5.5%
i 3766
 
5.2%
s 3345
 
4.6%
t 3087
 
4.3%
u 1872
 
2.6%
Other values (49) 25628
35.5%

Zip
Real number (ℝ)

Distinct8612
Distinct (%)86.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50159.324
Minimum610
Maximum99929
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-06T19:57:48.883031image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum610
5-th percentile6320
Q127592
median50207
Q372411.75
95-th percentile95629.1
Maximum99929
Range99319
Interquartile range (IQR)44819.75

Descriptive statistics

Standard deviation27469.588
Coefficient of variation (CV)0.5476467
Kurtosis-1.0653664
Mean50159.324
Median Absolute Deviation (MAD)22331
Skewness0.022812519
Sum5.0159324 × 108
Variance7.5457828 × 108
MonotonicityNot monotonic
2024-05-06T19:57:48.961082image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25674 4
 
< 0.1%
78104 4
 
< 0.1%
68355 4
 
< 0.1%
62098 4
 
< 0.1%
24136 4
 
< 0.1%
37324 4
 
< 0.1%
49791 4
 
< 0.1%
77663 4
 
< 0.1%
88345 4
 
< 0.1%
38330 4
 
< 0.1%
Other values (8602) 9960
99.6%
ValueCountFrequency (%)
610 1
< 0.1%
617 1
< 0.1%
622 1
< 0.1%
624 1
< 0.1%
631 1
< 0.1%
637 1
< 0.1%
656 2
< 0.1%
669 2
< 0.1%
670 1
< 0.1%
676 1
< 0.1%
ValueCountFrequency (%)
99929 1
< 0.1%
99925 1
< 0.1%
99922 1
< 0.1%
99919 1
< 0.1%
99918 1
< 0.1%
99833 1
< 0.1%
99791 1
< 0.1%
99790 2
< 0.1%
99786 1
< 0.1%
99785 2
< 0.1%

Lat
Real number (ℝ)

Distinct8588
Distinct (%)85.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.751099
Minimum17.96719
Maximum70.56099
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-06T19:57:49.036932image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum17.96719
5-th percentile29.835489
Q135.25512
median39.419355
Q342.044175
95-th percentile46.496091
Maximum70.56099
Range52.5938
Interquartile range (IQR)6.789055

Descriptive statistics

Standard deviation5.4030853
Coefficient of variation (CV)0.13943051
Kurtosis2.6841865
Mean38.751099
Median Absolute Deviation (MAD)3.23452
Skewness0.0085645322
Sum387510.99
Variance29.193331
MonotonicityNot monotonic
2024-05-06T19:57:49.116790image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.06702 4
 
< 0.1%
33.34798 4
 
< 0.1%
35.25512 4
 
< 0.1%
39.3861 4
 
< 0.1%
37.8689 4
 
< 0.1%
40.11509 4
 
< 0.1%
30.51239 4
 
< 0.1%
28.51879 4
 
< 0.1%
37.33123 4
 
< 0.1%
45.48682 4
 
< 0.1%
Other values (8578) 9960
99.6%
ValueCountFrequency (%)
17.96719 1
< 0.1%
17.99174 1
< 0.1%
18.01023 1
< 0.1%
18.03091 1
< 0.1%
18.0528 2
< 0.1%
18.06465 1
< 0.1%
18.06728 1
< 0.1%
18.07982 1
< 0.1%
18.097 1
< 0.1%
18.18555 1
< 0.1%
ValueCountFrequency (%)
70.56099 1
< 0.1%
70.1385 1
< 0.1%
67.47706 1
< 0.1%
67.17316 1
< 0.1%
67.11836 1
< 0.1%
66.97844 1
< 0.1%
66.88754 2
< 0.1%
66.76682 1
< 0.1%
66.56354 1
< 0.1%
66.3831 1
< 0.1%

Lng
Real number (ℝ)

Distinct8725
Distinct (%)87.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-91.24308
Minimum-174.2097
Maximum-65.29017
Zeros0
Zeros (%)0.0%
Negative10000
Negative (%)100.0%
Memory size78.3 KiB
2024-05-06T19:57:49.194453image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-174.2097
5-th percentile-121.4233
Q1-97.352982
median-88.39723
Q3-80.43805
95-th percentile-72.910863
Maximum-65.29017
Range108.91953
Interquartile range (IQR)16.914932

Descriptive statistics

Standard deviation15.205998
Coefficient of variation (CV)-0.16665371
Kurtosis2.3167004
Mean-91.24308
Median Absolute Deviation (MAD)8.40169
Skewness-1.2589728
Sum-912430.8
Variance231.22237
MonotonicityNot monotonic
2024-05-06T19:57:49.276978image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-90.30464 4
 
< 0.1%
-94.39542 4
 
< 0.1%
-80.59756 4
 
< 0.1%
-84.59579 4
 
< 0.1%
-95.56405 4
 
< 0.1%
-89.03658 4
 
< 0.1%
-97.76466 4
 
< 0.1%
-85.99134 4
 
< 0.1%
-82.35159 4
 
< 0.1%
-97.25468 3
 
< 0.1%
Other values (8715) 9961
99.6%
ValueCountFrequency (%)
-174.2097 1
< 0.1%
-171.68815 1
< 0.1%
-170.48517 1
< 0.1%
-166.49262 1
< 0.1%
-166.4926 1
< 0.1%
-165.80591 1
< 0.1%
-165.8059 1
< 0.1%
-165.7979 1
< 0.1%
-165.1285 1
< 0.1%
-164.4664 1
< 0.1%
ValueCountFrequency (%)
-65.29017 1
< 0.1%
-65.7274 2
< 0.1%
-65.76108 1
< 0.1%
-65.90881 1
< 0.1%
-65.94313 1
< 0.1%
-65.97632 1
< 0.1%
-65.97936 1
< 0.1%
-65.9921 1
< 0.1%
-66.01289 1
< 0.1%
-66.04267 1
< 0.1%

Population
Real number (ℝ)

ZEROS 

Distinct5951
Distinct (%)59.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9965.2538
Minimum0
Maximum122814
Zeros109
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-06T19:57:49.354600image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile102
Q1694.75
median2769
Q313945
95-th percentile41550
Maximum122814
Range122814
Interquartile range (IQR)13250.25

Descriptive statistics

Standard deviation14824.759
Coefficient of variation (CV)1.4876449
Kurtosis5.8809129
Mean9965.2538
Median Absolute Deviation (MAD)2541
Skewness2.2299586
Sum99652538
Variance2.1977347 × 108
MonotonicityNot monotonic
2024-05-06T19:57:49.434028image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 109
 
1.1%
195 14
 
0.1%
115 11
 
0.1%
178 11
 
0.1%
285 11
 
0.1%
955 10
 
0.1%
689 10
 
0.1%
92 10
 
0.1%
346 10
 
0.1%
294 10
 
0.1%
Other values (5941) 9794
97.9%
ValueCountFrequency (%)
0 109
1.1%
1 2
 
< 0.1%
4 1
 
< 0.1%
5 5
 
0.1%
6 5
 
0.1%
7 2
 
< 0.1%
8 1
 
< 0.1%
9 3
 
< 0.1%
10 2
 
< 0.1%
11 4
 
< 0.1%
ValueCountFrequency (%)
122814 2
< 0.1%
107700 1
< 0.1%
105799 2
< 0.1%
102624 1
< 0.1%
98219 1
< 0.1%
96530 2
< 0.1%
96487 1
< 0.1%
96081 1
< 0.1%
95666 1
< 0.1%
94512 1
< 0.1%

Area
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Rural
3369 
Suburban
3328 
Urban
3303 

Length

Max length8
Median length5
Mean length5.9984
Min length5

Characters and Unicode

Total characters59984
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSuburban
2nd rowUrban
3rd rowSuburban
4th rowSuburban
5th rowRural

Common Values

ValueCountFrequency (%)
Rural 3369
33.7%
Suburban 3328
33.3%
Urban 3303
33.0%

Length

2024-05-06T19:57:49.510351image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T19:57:49.574905image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
rural 3369
33.7%
suburban 3328
33.3%
urban 3303
33.0%

Most occurring characters

ValueCountFrequency (%)
u 10025
16.7%
r 10000
16.7%
a 10000
16.7%
b 9959
16.6%
n 6631
11.1%
R 3369
 
5.6%
l 3369
 
5.6%
S 3328
 
5.5%
U 3303
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 59984
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u 10025
16.7%
r 10000
16.7%
a 10000
16.7%
b 9959
16.6%
n 6631
11.1%
R 3369
 
5.6%
l 3369
 
5.6%
S 3328
 
5.5%
U 3303
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 59984
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u 10025
16.7%
r 10000
16.7%
a 10000
16.7%
b 9959
16.6%
n 6631
11.1%
R 3369
 
5.6%
l 3369
 
5.6%
S 3328
 
5.5%
U 3303
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 59984
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u 10025
16.7%
r 10000
16.7%
a 10000
16.7%
b 9959
16.6%
n 6631
11.1%
R 3369
 
5.6%
l 3369
 
5.6%
S 3328
 
5.5%
U 3303
 
5.5%

TimeZone
Categorical

IMBALANCE 

Distinct26
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
America/New_York
3889 
America/Chicago
3771 
America/Los_Angeles
937 
America/Denver
612 
America/Detroit
 
262
Other values (21)
529 

Length

Max length30
Median length28
Mean length15.9363
Min length12

Characters and Unicode

Total characters159363
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.1%

Sample

1st rowAmerica/Chicago
2nd rowAmerica/Chicago
3rd rowAmerica/Chicago
4th rowAmerica/Chicago
5th rowAmerica/New_York

Common Values

ValueCountFrequency (%)
America/New_York 3889
38.9%
America/Chicago 3771
37.7%
America/Los_Angeles 937
 
9.4%
America/Denver 612
 
6.1%
America/Detroit 262
 
2.6%
America/Indiana/Indianapolis 151
 
1.5%
America/Phoenix 100
 
1.0%
America/Boise 86
 
0.9%
America/Anchorage 50
 
0.5%
America/Puerto_Rico 43
 
0.4%
Other values (16) 99
 
1.0%

Length

2024-05-06T19:57:49.641925image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
america/new_york 3889
38.9%
america/chicago 3771
37.7%
america/los_angeles 937
 
9.4%
america/denver 612
 
6.1%
america/detroit 262
 
2.6%
america/indiana/indianapolis 151
 
1.5%
america/phoenix 100
 
1.0%
america/boise 86
 
0.9%
america/anchorage 50
 
0.5%
america/puerto_rico 43
 
0.4%
Other values (16) 99
 
1.0%

Most occurring characters

ValueCountFrequency (%)
e 17587
11.0%
r 14833
 
9.3%
i 14810
 
9.3%
a 14483
 
9.1%
c 13913
 
8.7%
A 10954
 
6.9%
/ 10177
 
6.4%
m 9994
 
6.3%
o 9457
 
5.9%
_ 4876
 
3.1%
Other values (31) 38279
24.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 159363
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 17587
11.0%
r 14833
 
9.3%
i 14810
 
9.3%
a 14483
 
9.1%
c 13913
 
8.7%
A 10954
 
6.9%
/ 10177
 
6.4%
m 9994
 
6.3%
o 9457
 
5.9%
_ 4876
 
3.1%
Other values (31) 38279
24.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 159363
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 17587
11.0%
r 14833
 
9.3%
i 14810
 
9.3%
a 14483
 
9.1%
c 13913
 
8.7%
A 10954
 
6.9%
/ 10177
 
6.4%
m 9994
 
6.3%
o 9457
 
5.9%
_ 4876
 
3.1%
Other values (31) 38279
24.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 159363
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 17587
11.0%
r 14833
 
9.3%
i 14810
 
9.3%
a 14483
 
9.1%
c 13913
 
8.7%
A 10954
 
6.9%
/ 10177
 
6.4%
m 9994
 
6.3%
o 9457
 
5.9%
_ 4876
 
3.1%
Other values (31) 38279
24.0%

Job
Text

Distinct639
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
2024-05-06T19:57:49.740887image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length59
Median length40
Mean length20.7697
Min length3

Characters and Unicode

Total characters207697
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPsychologist, sport and exercise
2nd rowCommunity development worker
3rd rowChief Executive Officer
4th rowEarly years teacher
5th rowHealth promotion specialist
ValueCountFrequency (%)
officer 936
 
4.1%
engineer 869
 
3.8%
manager 775
 
3.4%
scientist 416
 
1.8%
designer 392
 
1.7%
surveyor 314
 
1.4%
and 269
 
1.2%
teacher 244
 
1.1%
therapist 236
 
1.0%
research 235
 
1.0%
Other values (524) 18289
79.6%
2024-05-06T19:57:49.940418image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 21563
 
10.4%
i 17942
 
8.6%
r 17505
 
8.4%
a 15221
 
7.3%
t 14537
 
7.0%
n 14137
 
6.8%
12975
 
6.2%
o 11959
 
5.8%
s 11097
 
5.3%
c 10299
 
5.0%
Other values (44) 60462
29.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 207697
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 21563
 
10.4%
i 17942
 
8.6%
r 17505
 
8.4%
a 15221
 
7.3%
t 14537
 
7.0%
n 14137
 
6.8%
12975
 
6.2%
o 11959
 
5.8%
s 11097
 
5.3%
c 10299
 
5.0%
Other values (44) 60462
29.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 207697
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 21563
 
10.4%
i 17942
 
8.6%
r 17505
 
8.4%
a 15221
 
7.3%
t 14537
 
7.0%
n 14137
 
6.8%
12975
 
6.2%
o 11959
 
5.8%
s 11097
 
5.3%
c 10299
 
5.0%
Other values (44) 60462
29.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 207697
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 21563
 
10.4%
i 17942
 
8.6%
r 17505
 
8.4%
a 15221
 
7.3%
t 14537
 
7.0%
n 14137
 
6.8%
12975
 
6.2%
o 11959
 
5.8%
s 11097
 
5.3%
c 10299
 
5.0%
Other values (44) 60462
29.1%

Children
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0972
Minimum0
Maximum10
Zeros2548
Zeros (%)25.5%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-06T19:57:50.016504image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile7
Maximum10
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.163659
Coefficient of variation (CV)1.0316894
Kurtosis2.0763213
Mean2.0972
Median Absolute Deviation (MAD)1
Skewness1.4480126
Sum20972
Variance4.6814203
MonotonicityNot monotonic
2024-05-06T19:57:50.076411image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 2548
25.5%
1 2509
25.1%
3 1489
14.9%
2 1475
14.8%
4 995
 
10.0%
7 213
 
2.1%
8 209
 
2.1%
6 191
 
1.9%
5 169
 
1.7%
9 108
 
1.1%
ValueCountFrequency (%)
0 2548
25.5%
1 2509
25.1%
2 1475
14.8%
3 1489
14.9%
4 995
 
10.0%
5 169
 
1.7%
6 191
 
1.9%
7 213
 
2.1%
8 209
 
2.1%
9 108
 
1.1%
ValueCountFrequency (%)
10 94
 
0.9%
9 108
 
1.1%
8 209
 
2.1%
7 213
 
2.1%
6 191
 
1.9%
5 169
 
1.7%
4 995
 
10.0%
3 1489
14.9%
2 1475
14.8%
1 2509
25.1%

Age
Real number (ℝ)

Distinct72
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.5117
Minimum18
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-06T19:57:50.144410image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile21
Q136
median53
Q371
95-th percentile86
Maximum89
Range71
Interquartile range (IQR)35

Descriptive statistics

Standard deviation20.638538
Coefficient of variation (CV)0.38568272
Kurtosis-1.1895272
Mean53.5117
Median Absolute Deviation (MAD)18
Skewness0.0051171024
Sum535117
Variance425.94926
MonotonicityNot monotonic
2024-05-06T19:57:50.227410image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47 161
 
1.6%
52 159
 
1.6%
74 159
 
1.6%
41 157
 
1.6%
86 156
 
1.6%
66 155
 
1.6%
30 154
 
1.5%
40 154
 
1.5%
31 152
 
1.5%
34 151
 
1.5%
Other values (62) 8442
84.4%
ValueCountFrequency (%)
18 133
1.3%
19 137
1.4%
20 120
1.2%
21 125
1.2%
22 141
1.4%
23 137
1.4%
24 144
1.4%
25 130
1.3%
26 144
1.4%
27 135
1.4%
ValueCountFrequency (%)
89 132
1.3%
88 143
1.4%
87 136
1.4%
86 156
1.6%
85 135
1.4%
84 127
1.3%
83 134
1.3%
82 124
1.2%
81 131
1.3%
80 116
1.2%

Income
Real number (ℝ)

Distinct9993
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40490.495
Minimum154.08
Maximum207249.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-06T19:57:50.310414image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum154.08
5-th percentile7520.3535
Q119598.775
median33768.42
Q354296.402
95-th percentile96071.831
Maximum207249.1
Range207095.02
Interquartile range (IQR)34697.627

Descriptive statistics

Standard deviation28521.153
Coefficient of variation (CV)0.70439132
Kurtosis2.7456903
Mean40490.495
Median Absolute Deviation (MAD)16317.9
Skewness1.4058989
Sum4.0490495 × 108
Variance8.1345619 × 108
MonotonicityNot monotonic
2024-05-06T19:57:50.465024image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14572.4 2
 
< 0.1%
20474.03 2
 
< 0.1%
37132.97 2
 
< 0.1%
29508.62 2
 
< 0.1%
24997.02 2
 
< 0.1%
26915.85 2
 
< 0.1%
55506.92 2
 
< 0.1%
3532.5 1
 
< 0.1%
63176.93 1
 
< 0.1%
32371.65 1
 
< 0.1%
Other values (9983) 9983
99.8%
ValueCountFrequency (%)
154.08 1
< 0.1%
300.79 1
< 0.1%
395.23 1
< 0.1%
401.86 1
< 0.1%
493.04 1
< 0.1%
695.22 1
< 0.1%
702.16 1
< 0.1%
798.98 1
< 0.1%
826.01 1
< 0.1%
881.07 1
< 0.1%
ValueCountFrequency (%)
207249.1 1
< 0.1%
204542.41 1
< 0.1%
203774.6 1
< 0.1%
197675 1
< 0.1%
197576.18 1
< 0.1%
196915.6 1
< 0.1%
194796.24 1
< 0.1%
190110.8 1
< 0.1%
189416.27 1
< 0.1%
189129.92 1
< 0.1%

Marital
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Widowed
2045 
Married
2023 
Separated
1987 
Never Married
1984 
Divorced
1961 

Length

Max length13
Median length9
Mean length8.7839
Min length7

Characters and Unicode

Total characters87839
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDivorced
2nd rowMarried
3rd rowWidowed
4th rowMarried
5th rowWidowed

Common Values

ValueCountFrequency (%)
Widowed 2045
20.4%
Married 2023
20.2%
Separated 1987
19.9%
Never Married 1984
19.8%
Divorced 1961
19.6%

Length

2024-05-06T19:57:50.536057image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T19:57:50.598818image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
married 4007
33.4%
widowed 2045
17.1%
separated 1987
16.6%
never 1984
16.6%
divorced 1961
16.4%

Most occurring characters

ValueCountFrequency (%)
e 15955
18.2%
r 13946
15.9%
d 12045
13.7%
i 8013
9.1%
a 7981
9.1%
M 4007
 
4.6%
o 4006
 
4.6%
v 3945
 
4.5%
W 2045
 
2.3%
w 2045
 
2.3%
Other values (7) 13851
15.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 87839
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 15955
18.2%
r 13946
15.9%
d 12045
13.7%
i 8013
9.1%
a 7981
9.1%
M 4007
 
4.6%
o 4006
 
4.6%
v 3945
 
4.5%
W 2045
 
2.3%
w 2045
 
2.3%
Other values (7) 13851
15.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 87839
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 15955
18.2%
r 13946
15.9%
d 12045
13.7%
i 8013
9.1%
a 7981
9.1%
M 4007
 
4.6%
o 4006
 
4.6%
v 3945
 
4.5%
W 2045
 
2.3%
w 2045
 
2.3%
Other values (7) 13851
15.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 87839
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 15955
18.2%
r 13946
15.9%
d 12045
13.7%
i 8013
9.1%
a 7981
9.1%
M 4007
 
4.6%
o 4006
 
4.6%
v 3945
 
4.5%
W 2045
 
2.3%
w 2045
 
2.3%
Other values (7) 13851
15.8%

Gender
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Female
5018 
Male
4768 
Nonbinary
 
214

Length

Max length9
Median length6
Mean length5.1106
Min length4

Characters and Unicode

Total characters51106
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowFemale
3rd rowFemale
4th rowMale
5th rowFemale

Common Values

ValueCountFrequency (%)
Female 5018
50.2%
Male 4768
47.7%
Nonbinary 214
 
2.1%

Length

2024-05-06T19:57:50.670449image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T19:57:50.724822image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
female 5018
50.2%
male 4768
47.7%
nonbinary 214
 
2.1%

Most occurring characters

ValueCountFrequency (%)
e 14804
29.0%
a 10000
19.6%
l 9786
19.1%
F 5018
 
9.8%
m 5018
 
9.8%
M 4768
 
9.3%
n 428
 
0.8%
N 214
 
0.4%
o 214
 
0.4%
b 214
 
0.4%
Other values (3) 642
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 51106
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 14804
29.0%
a 10000
19.6%
l 9786
19.1%
F 5018
 
9.8%
m 5018
 
9.8%
M 4768
 
9.3%
n 428
 
0.8%
N 214
 
0.4%
o 214
 
0.4%
b 214
 
0.4%
Other values (3) 642
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 51106
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 14804
29.0%
a 10000
19.6%
l 9786
19.1%
F 5018
 
9.8%
m 5018
 
9.8%
M 4768
 
9.3%
n 428
 
0.8%
N 214
 
0.4%
o 214
 
0.4%
b 214
 
0.4%
Other values (3) 642
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 51106
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 14804
29.0%
a 10000
19.6%
l 9786
19.1%
F 5018
 
9.8%
m 5018
 
9.8%
M 4768
 
9.3%
n 428
 
0.8%
N 214
 
0.4%
o 214
 
0.4%
b 214
 
0.4%
Other values (3) 642
 
1.3%

ReAdmis
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
False
6331 
True
3669 
ValueCountFrequency (%)
False 6331
63.3%
True 3669
36.7%
2024-05-06T19:57:50.774668image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

VitD_levels
Real number (ℝ)

Distinct9976
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.964262
Minimum9.806483
Maximum26.394449
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-06T19:57:50.835733image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum9.806483
5-th percentile14.655531
Q116.626439
median17.951122
Q319.347963
95-th percentile21.304579
Maximum26.394449
Range16.587966
Interquartile range (IQR)2.7215239

Descriptive statistics

Standard deviation2.017231
Coefficient of variation (CV)0.11229134
Kurtosis-0.022111964
Mean17.964262
Median Absolute Deviation (MAD)1.3605081
Skewness0.032434968
Sum179642.62
Variance4.0692211
MonotonicityNot monotonic
2024-05-06T19:57:50.916891image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.13543091 2
 
< 0.1%
15.93976 2
 
< 0.1%
17.82186 2
 
< 0.1%
20.18417 2
 
< 0.1%
18.74134 2
 
< 0.1%
16.55326 2
 
< 0.1%
17.93487 2
 
< 0.1%
16.78339 2
 
< 0.1%
18.71128 2
 
< 0.1%
15.26009 2
 
< 0.1%
Other values (9966) 9980
99.8%
ValueCountFrequency (%)
9.806483 1
< 0.1%
10.31523421 1
< 0.1%
10.87742702 1
< 0.1%
11.08343 1
< 0.1%
11.47531413 1
< 0.1%
11.53873 1
< 0.1%
11.75106 1
< 0.1%
11.75556 1
< 0.1%
11.78539496 1
< 0.1%
11.84683 1
< 0.1%
ValueCountFrequency (%)
26.39444871 1
< 0.1%
25.4440994 1
< 0.1%
25.14727 1
< 0.1%
24.88911 1
< 0.1%
24.63742 1
< 0.1%
24.56546284 1
< 0.1%
24.55913175 1
< 0.1%
24.53871 1
< 0.1%
24.45037 1
< 0.1%
24.421656 1
< 0.1%

Doc_visits
Real number (ℝ)

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0122
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-06T19:57:50.981266image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median5
Q36
95-th percentile7
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.0457344
Coefficient of variation (CV)0.20863781
Kurtosis0.025999275
Mean5.0122
Median Absolute Deviation (MAD)1
Skewness-0.018563292
Sum50122
Variance1.0935605
MonotonicityNot monotonic
2024-05-06T19:57:51.038724image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
5 3823
38.2%
6 2436
24.4%
4 2385
23.8%
7 634
 
6.3%
3 595
 
5.9%
8 61
 
0.6%
2 58
 
0.6%
1 6
 
0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
1 6
 
0.1%
2 58
 
0.6%
3 595
 
5.9%
4 2385
23.8%
5 3823
38.2%
6 2436
24.4%
7 634
 
6.3%
8 61
 
0.6%
9 2
 
< 0.1%
ValueCountFrequency (%)
9 2
 
< 0.1%
8 61
 
0.6%
7 634
 
6.3%
6 2436
24.4%
5 3823
38.2%
4 2385
23.8%
3 595
 
5.9%
2 58
 
0.6%
1 6
 
0.1%

Full_meals_eaten
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0014
Minimum0
Maximum7
Zeros3715
Zeros (%)37.1%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-06T19:57:51.094751image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum7
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.0081169
Coefficient of variation (CV)1.0067075
Kurtosis1.0427267
Mean1.0014
Median Absolute Deviation (MAD)1
Skewness1.0094611
Sum10014
Variance1.0162997
MonotonicityNot monotonic
2024-05-06T19:57:51.153607image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 3715
37.1%
1 3615
36.1%
2 1856
18.6%
3 612
 
6.1%
4 169
 
1.7%
5 25
 
0.2%
6 6
 
0.1%
7 2
 
< 0.1%
ValueCountFrequency (%)
0 3715
37.1%
1 3615
36.1%
2 1856
18.6%
3 612
 
6.1%
4 169
 
1.7%
5 25
 
0.2%
6 6
 
0.1%
7 2
 
< 0.1%
ValueCountFrequency (%)
7 2
 
< 0.1%
6 6
 
0.1%
5 25
 
0.2%
4 169
 
1.7%
3 612
 
6.1%
2 1856
18.6%
1 3615
36.1%
0 3715
37.1%

vitD_supp
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3989
Minimum0
Maximum5
Zeros6702
Zeros (%)67.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-06T19:57:51.210063image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.62850481
Coefficient of variation (CV)1.5755949
Kurtosis2.3307634
Mean0.3989
Median Absolute Deviation (MAD)0
Skewness1.5502054
Sum3989
Variance0.39501829
MonotonicityNot monotonic
2024-05-06T19:57:51.268891image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 6702
67.0%
1 2684
26.8%
2 544
 
5.4%
3 64
 
0.6%
4 5
 
0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
0 6702
67.0%
1 2684
26.8%
2 544
 
5.4%
3 64
 
0.6%
4 5
 
0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
5 1
 
< 0.1%
4 5
 
0.1%
3 64
 
0.6%
2 544
 
5.4%
1 2684
26.8%
0 6702
67.0%

Soft_drink
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
False
7425 
True
2575 
ValueCountFrequency (%)
False 7425
74.2%
True 2575
 
25.8%
2024-05-06T19:57:51.319815image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Initial_admin
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Emergency Admission
5060 
Elective Admission
2504 
Observation Admission
2436 

Length

Max length21
Median length19
Mean length19.2368
Min length18

Characters and Unicode

Total characters192368
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEmergency Admission
2nd rowEmergency Admission
3rd rowElective Admission
4th rowElective Admission
5th rowElective Admission

Common Values

ValueCountFrequency (%)
Emergency Admission 5060
50.6%
Elective Admission 2504
25.0%
Observation Admission 2436
24.4%

Length

2024-05-06T19:57:51.380160image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T19:57:51.435314image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
admission 10000
50.0%
emergency 5060
25.3%
elective 2504
 
12.5%
observation 2436
 
12.2%

Most occurring characters

ValueCountFrequency (%)
i 24940
13.0%
s 22436
11.7%
e 17564
 
9.1%
n 17496
 
9.1%
m 15060
 
7.8%
o 12436
 
6.5%
d 10000
 
5.2%
A 10000
 
5.2%
10000
 
5.2%
E 7564
 
3.9%
Other values (10) 44872
23.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 192368
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 24940
13.0%
s 22436
11.7%
e 17564
 
9.1%
n 17496
 
9.1%
m 15060
 
7.8%
o 12436
 
6.5%
d 10000
 
5.2%
A 10000
 
5.2%
10000
 
5.2%
E 7564
 
3.9%
Other values (10) 44872
23.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 192368
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 24940
13.0%
s 22436
11.7%
e 17564
 
9.1%
n 17496
 
9.1%
m 15060
 
7.8%
o 12436
 
6.5%
d 10000
 
5.2%
A 10000
 
5.2%
10000
 
5.2%
E 7564
 
3.9%
Other values (10) 44872
23.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 192368
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 24940
13.0%
s 22436
11.7%
e 17564
 
9.1%
n 17496
 
9.1%
m 15060
 
7.8%
o 12436
 
6.5%
d 10000
 
5.2%
A 10000
 
5.2%
10000
 
5.2%
E 7564
 
3.9%
Other values (10) 44872
23.3%

HighBlood
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
False
5910 
True
4090 
ValueCountFrequency (%)
False 5910
59.1%
True 4090
40.9%
2024-05-06T19:57:51.486925image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Stroke
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
False
8007 
True
1993 
ValueCountFrequency (%)
False 8007
80.1%
True 1993
 
19.9%
2024-05-06T19:57:51.533337image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Medium
4517 
High
3358 
Low
2125 

Length

Max length6
Median length4
Mean length4.6909
Min length3

Characters and Unicode

Total characters46909
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedium
2nd rowHigh
3rd rowMedium
4th rowMedium
5th rowLow

Common Values

ValueCountFrequency (%)
Medium 4517
45.2%
High 3358
33.6%
Low 2125
21.2%

Length

2024-05-06T19:57:51.592862image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T19:57:51.649052image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
medium 4517
45.2%
high 3358
33.6%
low 2125
21.2%

Most occurring characters

ValueCountFrequency (%)
i 7875
16.8%
M 4517
9.6%
e 4517
9.6%
d 4517
9.6%
u 4517
9.6%
m 4517
9.6%
H 3358
7.2%
g 3358
7.2%
h 3358
7.2%
L 2125
 
4.5%
Other values (2) 4250
9.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 46909
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 7875
16.8%
M 4517
9.6%
e 4517
9.6%
d 4517
9.6%
u 4517
9.6%
m 4517
9.6%
H 3358
7.2%
g 3358
7.2%
h 3358
7.2%
L 2125
 
4.5%
Other values (2) 4250
9.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 46909
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 7875
16.8%
M 4517
9.6%
e 4517
9.6%
d 4517
9.6%
u 4517
9.6%
m 4517
9.6%
H 3358
7.2%
g 3358
7.2%
h 3358
7.2%
L 2125
 
4.5%
Other values (2) 4250
9.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 46909
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 7875
16.8%
M 4517
9.6%
e 4517
9.6%
d 4517
9.6%
u 4517
9.6%
m 4517
9.6%
H 3358
7.2%
g 3358
7.2%
h 3358
7.2%
L 2125
 
4.5%
Other values (2) 4250
9.1%

Overweight
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
True
7094 
False
2906 
ValueCountFrequency (%)
True 7094
70.9%
False 2906
29.1%
2024-05-06T19:57:51.698053image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Arthritis
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
False
6426 
True
3574 
ValueCountFrequency (%)
False 6426
64.3%
True 3574
35.7%
2024-05-06T19:57:51.743252image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Diabetes
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
False
7262 
True
2738 
ValueCountFrequency (%)
False 7262
72.6%
True 2738
 
27.4%
2024-05-06T19:57:51.788584image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
False
6628 
True
3372 
ValueCountFrequency (%)
False 6628
66.3%
True 3372
33.7%
2024-05-06T19:57:51.833117image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

BackPain
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
False
5886 
True
4114 
ValueCountFrequency (%)
False 5886
58.9%
True 4114
41.1%
2024-05-06T19:57:51.877824image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Anxiety
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
False
6785 
True
3215 
ValueCountFrequency (%)
False 6785
67.8%
True 3215
32.1%
2024-05-06T19:57:51.924515image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
False
6059 
True
3941 
ValueCountFrequency (%)
False 6059
60.6%
True 3941
39.4%
2024-05-06T19:57:51.969133image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
False
5865 
True
4135 
ValueCountFrequency (%)
False 5865
58.7%
True 4135
41.3%
2024-05-06T19:57:52.014866image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Asthma
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 KiB
False
7107 
True
2893 
ValueCountFrequency (%)
False 7107
71.1%
True 2893
28.9%
2024-05-06T19:57:52.060799image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Services
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.3 KiB
Blood Work
5265 
Intravenous
3130 
CT Scan
1225 
MRI
 
380

Length

Max length11
Median length10
Mean length9.6795
Min length3

Characters and Unicode

Total characters96795
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBlood Work
2nd rowIntravenous
3rd rowBlood Work
4th rowBlood Work
5th rowCT Scan

Common Values

ValueCountFrequency (%)
Blood Work 5265
52.6%
Intravenous 3130
31.3%
CT Scan 1225
 
12.2%
MRI 380
 
3.8%

Length

2024-05-06T19:57:52.120863image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-06T19:57:52.181434image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
blood 5265
31.9%
work 5265
31.9%
intravenous 3130
19.0%
ct 1225
 
7.4%
scan 1225
 
7.4%
mri 380
 
2.3%

Most occurring characters

ValueCountFrequency (%)
o 18925
19.6%
r 8395
 
8.7%
n 7485
 
7.7%
6490
 
6.7%
B 5265
 
5.4%
d 5265
 
5.4%
W 5265
 
5.4%
k 5265
 
5.4%
l 5265
 
5.4%
a 4355
 
4.5%
Other values (12) 24820
25.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 96795
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 18925
19.6%
r 8395
 
8.7%
n 7485
 
7.7%
6490
 
6.7%
B 5265
 
5.4%
d 5265
 
5.4%
W 5265
 
5.4%
k 5265
 
5.4%
l 5265
 
5.4%
a 4355
 
4.5%
Other values (12) 24820
25.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 96795
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 18925
19.6%
r 8395
 
8.7%
n 7485
 
7.7%
6490
 
6.7%
B 5265
 
5.4%
d 5265
 
5.4%
W 5265
 
5.4%
k 5265
 
5.4%
l 5265
 
5.4%
a 4355
 
4.5%
Other values (12) 24820
25.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 96795
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 18925
19.6%
r 8395
 
8.7%
n 7485
 
7.7%
6490
 
6.7%
B 5265
 
5.4%
d 5265
 
5.4%
W 5265
 
5.4%
k 5265
 
5.4%
l 5265
 
5.4%
a 4355
 
4.5%
Other values (12) 24820
25.6%

Initial_days
Real number (ℝ)

Distinct9997
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.455299
Minimum1.0019809
Maximum71.98149
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-06T19:57:52.249366image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1.0019809
5-th percentile2.3081027
Q17.8962147
median35.836244
Q361.16102
95-th percentile69.892048
Maximum71.98149
Range70.979509
Interquartile range (IQR)53.264805

Descriptive statistics

Standard deviation26.309341
Coefficient of variation (CV)0.76357895
Kurtosis-1.7545246
Mean34.455299
Median Absolute Deviation (MAD)26.863318
Skewness0.070286083
Sum344552.99
Variance692.18144
MonotonicityNot monotonic
2024-05-06T19:57:52.328012image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63.54432 2
 
< 0.1%
67.42139 2
 
< 0.1%
70.32542 2
 
< 0.1%
63.33469 1
 
< 0.1%
67.03651 1
 
< 0.1%
64.53351 1
 
< 0.1%
49.52823 1
 
< 0.1%
66.01216 1
 
< 0.1%
67.60782 1
 
< 0.1%
62.9642 1
 
< 0.1%
Other values (9987) 9987
99.9%
ValueCountFrequency (%)
1.001980919 1
< 0.1%
1.008401065 1
< 0.1%
1.009142748 1
< 0.1%
1.010068463 1
< 0.1%
1.012481219 1
< 0.1%
1.012585747 1
< 0.1%
1.013972797 1
< 0.1%
1.014903071 1
< 0.1%
1.016185458 1
< 0.1%
1.016258363 1
< 0.1%
ValueCountFrequency (%)
71.98149 1
< 0.1%
71.96869 1
< 0.1%
71.96546 1
< 0.1%
71.96415 1
< 0.1%
71.96342 1
< 0.1%
71.96164 1
< 0.1%
71.96134 1
< 0.1%
71.95813 1
< 0.1%
71.95472 1
< 0.1%
71.94732 1
< 0.1%

TotalCharge
Real number (ℝ)

Distinct9997
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5312.1728
Minimum1938.3121
Maximum9180.728
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-06T19:57:52.408866image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1938.3121
5-th percentile2521.0829
Q13179.374
median5213.952
Q37459.6997
95-th percentile8342.4286
Maximum9180.728
Range7242.4159
Interquartile range (IQR)4280.3257

Descriptive statistics

Standard deviation2180.3938
Coefficient of variation (CV)0.41045236
Kurtosis-1.6682665
Mean5312.1728
Median Absolute Deviation (MAD)2129.2794
Skewness0.069660946
Sum53121728
Variance4754117.3
MonotonicityNot monotonic
2024-05-06T19:57:52.484854image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7555.452 2
 
< 0.1%
7964.681 2
 
< 0.1%
8081.346 2
 
< 0.1%
3726.70286 1
 
< 0.1%
8449.859 1
 
< 0.1%
7174.396 1
 
< 0.1%
7358.173 1
 
< 0.1%
6390.429 1
 
< 0.1%
7548.257 1
 
< 0.1%
7284.532 1
 
< 0.1%
Other values (9987) 9987
99.9%
ValueCountFrequency (%)
1938.312067 1
< 0.1%
1957.445547 1
< 0.1%
1969.472468 1
< 0.1%
2000.776336 1
< 0.1%
2004.755279 1
< 0.1%
2006.878935 1
< 0.1%
2010.399554 1
< 0.1%
2022.650007 1
< 0.1%
2027.22267 1
< 0.1%
2035.787412 1
< 0.1%
ValueCountFrequency (%)
9180.728 1
< 0.1%
9169.248 1
< 0.1%
9080.912 1
< 0.1%
9077.388 1
< 0.1%
9067.605 1
< 0.1%
9065.054 1
< 0.1%
9028.118 1
< 0.1%
9022.166 1
< 0.1%
9012.388 1
< 0.1%
9004.401 1
< 0.1%

Additional_charges
Real number (ℝ)

Distinct9418
Distinct (%)94.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12934.529
Minimum3125.703
Maximum30566.07
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-06T19:57:52.563451image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum3125.703
5-th percentile4608.275
Q17986.4878
median11573.978
Q315626.49
95-th percentile26604.555
Maximum30566.07
Range27440.367
Interquartile range (IQR)7640.0022

Descriptive statistics

Standard deviation6542.6015
Coefficient of variation (CV)0.50582451
Kurtosis-0.14268428
Mean12934.529
Median Absolute Deviation (MAD)3716.8143
Skewness0.831842
Sum1.2934529 × 108
Variance42805635
MonotonicityNot monotonic
2024-05-06T19:57:52.635079image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24109.57264 3
 
< 0.1%
25325.81647 3
 
< 0.1%
6315.62213 3
 
< 0.1%
11995.00516 3
 
< 0.1%
4880.460246 3
 
< 0.1%
5000.124 3
 
< 0.1%
10299.30056 3
 
< 0.1%
14644.12 3
 
< 0.1%
13791.41 3
 
< 0.1%
14641.57104 3
 
< 0.1%
Other values (9408) 9970
99.7%
ValueCountFrequency (%)
3125.703 1
< 0.1%
3132.25999 2
< 0.1%
3139.049369 2
< 0.1%
3173.112679 1
< 0.1%
3213.0799 1
< 0.1%
3214.27887 1
< 0.1%
3221.335 1
< 0.1%
3241.33976 2
< 0.1%
3241.34 2
< 0.1%
3243.864573 1
< 0.1%
ValueCountFrequency (%)
30566.07 1
< 0.1%
30466.93 1
< 0.1%
30422.53 1
< 0.1%
30395.02524 1
< 0.1%
30087.65094 1
< 0.1%
30071.48531 1
< 0.1%
29981.79494 1
< 0.1%
29885.66 1
< 0.1%
29837.70749 1
< 0.1%
29828.95899 1
< 0.1%

Item1
Real number (ℝ)

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5188
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-06T19:57:52.694840image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q34
95-th percentile5
Maximum8
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0319656
Coefficient of variation (CV)0.29327203
Kurtosis-0.086467871
Mean3.5188
Median Absolute Deviation (MAD)1
Skewness0.029341065
Sum35188
Variance1.0649531
MonotonicityNot monotonic
2024-05-06T19:57:52.755830image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
4 3455
34.5%
3 3404
34.0%
5 1377
 
13.8%
2 1315
 
13.2%
6 225
 
2.2%
1 213
 
2.1%
7 10
 
0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
1 213
 
2.1%
2 1315
 
13.2%
3 3404
34.0%
4 3455
34.5%
5 1377
 
13.8%
6 225
 
2.2%
7 10
 
0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 10
 
0.1%
6 225
 
2.2%
5 1377
 
13.8%
4 3455
34.5%
3 3404
34.0%
2 1315
 
13.2%
1 213
 
2.1%

Item2
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5067
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-06T19:57:52.812472image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0348247
Coefficient of variation (CV)0.2950993
Kurtosis-0.15777178
Mean3.5067
Median Absolute Deviation (MAD)1
Skewness0.031911727
Sum35067
Variance1.0708622
MonotonicityNot monotonic
2024-05-06T19:57:52.867826image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 3439
34.4%
4 3351
33.5%
5 1421
14.2%
2 1360
 
13.6%
1 213
 
2.1%
6 204
 
2.0%
7 12
 
0.1%
ValueCountFrequency (%)
1 213
 
2.1%
2 1360
 
13.6%
3 3439
34.4%
4 3351
33.5%
5 1421
14.2%
6 204
 
2.0%
7 12
 
0.1%
ValueCountFrequency (%)
7 12
 
0.1%
6 204
 
2.0%
5 1421
14.2%
4 3351
33.5%
3 3439
34.4%
2 1360
 
13.6%
1 213
 
2.1%

Item3
Real number (ℝ)

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5111
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-06T19:57:53.002544image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q34
95-th percentile5
Maximum8
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0327553
Coefficient of variation (CV)0.29414009
Kurtosis-0.094517211
Mean3.5111
Median Absolute Deviation (MAD)1
Skewness0.033200429
Sum35111
Variance1.0665834
MonotonicityNot monotonic
2024-05-06T19:57:53.059566image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
4 3464
34.6%
3 3379
33.8%
5 1358
 
13.6%
2 1356
 
13.6%
6 220
 
2.2%
1 211
 
2.1%
7 11
 
0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
1 211
 
2.1%
2 1356
 
13.6%
3 3379
33.8%
4 3464
34.6%
5 1358
 
13.6%
6 220
 
2.2%
7 11
 
0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 11
 
0.1%
6 220
 
2.2%
5 1358
 
13.6%
4 3464
34.6%
3 3379
33.8%
2 1356
 
13.6%
1 211
 
2.1%

Item4
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5151
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-06T19:57:53.116004image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q34
95-th percentile5
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0362815
Coefficient of variation (CV)0.29480854
Kurtosis-0.12608848
Mean3.5151
Median Absolute Deviation (MAD)1
Skewness0.048655569
Sum35151
Variance1.0738794
MonotonicityNot monotonic
2024-05-06T19:57:53.171002image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 3422
34.2%
4 3394
33.9%
5 1388
13.9%
2 1346
 
13.5%
6 231
 
2.3%
1 207
 
2.1%
7 12
 
0.1%
ValueCountFrequency (%)
1 207
 
2.1%
2 1346
 
13.5%
3 3422
34.2%
4 3394
33.9%
5 1388
13.9%
6 231
 
2.3%
7 12
 
0.1%
ValueCountFrequency (%)
7 12
 
0.1%
6 231
 
2.3%
5 1388
13.9%
4 3394
33.9%
3 3422
34.2%
2 1346
 
13.5%
1 207
 
2.1%

Item5
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4969
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-06T19:57:53.225829image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0301925
Coefficient of variation (CV)0.29460164
Kurtosis-0.087591745
Mean3.4969
Median Absolute Deviation (MAD)1
Skewness0.04949087
Sum34969
Variance1.0612965
MonotonicityNot monotonic
2024-05-06T19:57:53.281772image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 3446
34.5%
3 3423
34.2%
2 1380
13.8%
5 1308
 
13.1%
6 219
 
2.2%
1 211
 
2.1%
7 13
 
0.1%
ValueCountFrequency (%)
1 211
 
2.1%
2 1380
13.8%
3 3423
34.2%
4 3446
34.5%
5 1308
 
13.1%
6 219
 
2.2%
7 13
 
0.1%
ValueCountFrequency (%)
7 13
 
0.1%
6 219
 
2.2%
5 1308
 
13.1%
4 3446
34.5%
3 3423
34.2%
2 1380
13.8%
1 211
 
2.1%

Item6
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5225
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-06T19:57:53.334819image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q34
95-th percentile5
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0323761
Coefficient of variation (CV)0.2930805
Kurtosis-0.13081223
Mean3.5225
Median Absolute Deviation (MAD)1
Skewness0.01034502
Sum35225
Variance1.0658003
MonotonicityNot monotonic
2024-05-06T19:57:53.390716image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 3464
34.6%
3 3371
33.7%
5 1403
14.0%
2 1319
 
13.2%
6 220
 
2.2%
1 213
 
2.1%
7 10
 
0.1%
ValueCountFrequency (%)
1 213
 
2.1%
2 1319
 
13.2%
3 3371
33.7%
4 3464
34.6%
5 1403
14.0%
6 220
 
2.2%
7 10
 
0.1%
ValueCountFrequency (%)
7 10
 
0.1%
6 220
 
2.2%
5 1403
14.0%
4 3464
34.6%
3 3371
33.7%
2 1319
 
13.2%
1 213
 
2.1%

Item7
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.494
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-06T19:57:53.442710image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0214051
Coefficient of variation (CV)0.29233116
Kurtosis-0.056030688
Mean3.494
Median Absolute Deviation (MAD)1
Skewness0.035368327
Sum34940
Variance1.0432683
MonotonicityNot monotonic
2024-05-06T19:57:53.495704image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 3487
34.9%
3 3456
34.6%
2 1345
 
13.5%
5 1274
 
12.7%
1 215
 
2.1%
6 212
 
2.1%
7 11
 
0.1%
ValueCountFrequency (%)
1 215
 
2.1%
2 1345
 
13.5%
3 3456
34.6%
4 3487
34.9%
5 1274
 
12.7%
6 212
 
2.1%
7 11
 
0.1%
ValueCountFrequency (%)
7 11
 
0.1%
6 212
 
2.1%
5 1274
 
12.7%
4 3487
34.9%
3 3456
34.6%
2 1345
 
13.5%
1 215
 
2.1%

Item8
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5097
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.3 KiB
2024-05-06T19:57:53.548699image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0423121
Coefficient of variation (CV)0.2969804
Kurtosis-0.18020853
Mean3.5097
Median Absolute Deviation (MAD)1
Skewness0.044709052
Sum35097
Variance1.0864146
MonotonicityNot monotonic
2024-05-06T19:57:53.602693image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 3401
34.0%
4 3337
33.4%
5 1429
14.3%
2 1391
13.9%
6 221
 
2.2%
1 209
 
2.1%
7 12
 
0.1%
ValueCountFrequency (%)
1 209
 
2.1%
2 1391
13.9%
3 3401
34.0%
4 3337
33.4%
5 1429
14.3%
6 221
 
2.2%
7 12
 
0.1%
ValueCountFrequency (%)
7 12
 
0.1%
6 221
 
2.2%
5 1429
14.3%
4 3337
33.4%
3 3401
34.0%
2 1391
13.9%
1 209
 
2.1%

Interactions

2024-05-06T19:57:44.705448image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:15.635600image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:16.896723image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:18.211442image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:19.444440image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:20.765403image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:22.055145image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:23.376389image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:24.759563image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:26.002826image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:27.311078image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:28.706285image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:30.089686image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:31.341842image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:32.712160image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:34.049945image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:35.426268image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:36.724499image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:38.088578image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:39.393269image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:40.766360image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:42.038684image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:43.410211image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:44.762866image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:15.693706image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:16.947833image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:18.262777image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:19.495323image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:20.818388image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:22.185222image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:23.429368image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:24.810614image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:26.058352image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:27.369282image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:28.760024image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:30.138685image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:31.397840image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:32.767262image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:34.102046image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:35.482249image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:36.777498image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:38.143473image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:39.446285image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:40.821718image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:42.094062image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:43.463195image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:44.813869image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:15.745592image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:17.064706image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:18.314715image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:19.620668image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:20.872388image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:22.239137image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:23.484354image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:24.862495image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:26.113604image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:27.504110image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:28.815006image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:30.190688image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:31.455778image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:32.823162image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:34.156577image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:35.537828image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:36.831493image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:38.198494image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:39.498378image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:40.877763image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:42.145727image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:43.516189image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:44.864917image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:15.797580image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:17.117726image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:18.366577image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:19.673388image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:20.925387image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:22.293043image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:23.533369image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:24.914120image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:26.166654image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:27.559109image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:28.871851image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:30.243308image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:31.509667image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:32.878275image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:34.212697image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:35.593419image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:36.882509image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:38.253544image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:39.554393image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:40.932103image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:42.201177image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:43.568172image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:44.919287image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:15.852576image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:17.171694image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:18.416615image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:19.723388image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:20.980470image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:22.345114image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:23.587369image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:24.967118image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:26.219797image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:27.613110image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:28.927168image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:30.293297image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:31.564333image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:32.935279image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:34.267280image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:35.650286image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:36.935321image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:38.309469image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:39.612128image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:40.989737image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:42.254096image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:43.626174image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:44.974849image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:15.909606image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:17.224231image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:18.474612image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:19.775388image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:21.036525image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:22.401362image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:23.643621image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:25.021324image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:26.278803image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:27.671111image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:28.985632image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:30.350551image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:31.619697image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:32.995227image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:34.326362image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:35.706384image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:36.991462image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:38.368055image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:39.669129image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:41.045735image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:42.308095image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:43.685173image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:45.029665image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:15.961567image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:17.276223image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:18.524603image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:19.826389image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:21.090168image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:22.453406image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:23.700434image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:25.073326image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:26.331805image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:27.725111image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:29.040633image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:30.400876image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:31.671958image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:33.052209image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:34.380769image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:35.763256image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:37.046003image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:38.421177image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:39.723313image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:41.103308image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:42.362654image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:43.740914image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:45.088324image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:16.016561image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-05-06T19:57:40.311019image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:41.610038image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:42.963440image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:44.259156image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:45.677333image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:16.512795image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:17.832081image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:19.069894image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:20.380388image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:21.661020image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:23.003514image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:24.286497image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:25.617045image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:26.903018image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:28.302738image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:29.617546image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:30.964687image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:32.244358image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:33.631281image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:34.957752image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:36.339805image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:37.705457image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:38.996822image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:40.366019image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:41.665395image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:43.022278image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:44.316878image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:45.732334image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:16.566792image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:17.884600image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:19.123271image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:20.436403image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:21.717022image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:23.057353image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:24.345496image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:25.671365image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:26.962438image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:28.360755image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:29.674045image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:31.016389image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:32.298337image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:33.692392image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:35.013603image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:36.395935image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:37.758178image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:39.051703image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:40.419019image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:41.721846image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:43.080168image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:44.374125image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:45.786442image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:16.622789image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:17.940531image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:19.182287image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:20.492387image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:21.775631image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:23.115369image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:24.403961image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:25.728201image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:27.022222image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:28.418077image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:29.732654image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:31.071180image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:32.353917image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:33.752367image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:35.068603image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:36.453918image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:37.813247image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:39.108978image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:40.474018image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:41.775326image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:43.137810image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:44.430126image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:45.839885image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:16.674763image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:17.995567image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:19.233912image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:20.549404image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:21.832546image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:23.167368image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:24.461151image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:25.781223image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:27.076223image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:28.478450image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:29.790687image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:31.125232image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:32.409260image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:33.811043image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:35.204013image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:36.510498image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:37.867145image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:39.164978image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:40.526022image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:41.825431image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:43.192274image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:44.485532image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:45.895245image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:16.728770image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:18.049556image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:19.286433image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:20.604389image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:21.888996image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:23.219369image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:24.515213image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:25.834741image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:27.133842image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:28.535450image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:29.924685image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:31.177732image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:32.544901image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:33.869043image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:35.260438image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:36.564509image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:37.923136image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:39.225087image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:40.587018image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:41.879697image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:43.249146image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:44.539810image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:45.951020image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:16.782685image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:18.104470image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:19.338539image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:20.658387image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:21.946426image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:23.270369image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:24.570961image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:25.892045image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:27.192091image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:28.593712image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:29.980686image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:31.231471image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:32.599902image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:33.928414image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:35.317047image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:36.618509image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:37.979134image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:39.280193image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:40.652018image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:41.930572image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:43.303189image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:44.594485image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:46.005063image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:16.840681image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:18.157458image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:19.393062image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:20.711404image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:21.999446image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:23.323355image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:24.627561image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:25.947698image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:27.257075image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:28.648833image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:30.034701image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:31.285164image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:32.654161image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:33.993413image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:35.372252image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:36.670509image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:38.033198image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:39.337285image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:40.710943image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:41.984812image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:43.355188image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-05-06T19:57:44.648818image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-05-06T19:57:46.133123image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-06T19:57:46.466308image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CaseOrderCustomer_idInteractionUIDCityStateCountyZipLatLngPopulationAreaTimeZoneJobChildrenAgeIncomeMaritalGenderReAdmisVitD_levelsDoc_visitsFull_meals_eatenvitD_suppSoft_drinkInitial_adminHighBloodStrokeComplication_riskOverweightArthritisDiabetesHyperlipidemiaBackPainAnxietyAllergic_rhinitisReflux_esophagitisAsthmaServicesInitial_daysTotalChargeAdditional_chargesItem1Item2Item3Item4Item5Item6Item7Item8
01C4124038cd49b13-f45a-4b47-a2bd-173ffa932c2f3a83ddb66e2ae73798bdf1d705dc0932EvaALMorgan3562134.34960-86.725082951SuburbanAmerica/ChicagoPsychologist, sport and exercise15386575.93DivorcedMaleNo19.141466600NoEmergency AdmissionYesNoMediumNoYesYesNoYesYesYesNoYesBlood Work10.5857703726.70286017939.40342033224334
12Z919181d2450b70-0337-4406-bdbb-bc1037f1734c176354c5eef714957d486009feabf195MariannaFLJackson3244630.84513-85.2290711303UrbanAmerica/ChicagoCommunity development worker35146805.99MarriedFemaleNo18.940352421NoEmergency AdmissionYesNoHighYesNoNoNoNoNoNoYesNoIntravenous15.1295624193.19045817612.99812034344433
23F995323a2057123-abf5-4a2c-abad-8ffe33512562e19a0fa00aeda885b8a436757e889bc9Sioux FallsSDMinnehaha5711043.54321-96.6377217125SuburbanAmerica/ChicagoChief Executive Officer35314370.14WidowedFemaleNo18.057507410NoElective AdmissionYesNoMediumYesNoYesNoNoNoNoNoNoBlood Work4.7721772434.23422217505.19246024443433
34A8799731dec528d-eb34-4079-adce-0d7a40e82205cd17d7b6d152cb6f23957346d11c3f07New RichlandMNWaseca5607243.89744-93.514792162SuburbanAmerica/ChicagoEarly years teacher07839741.49MarriedMaleNo16.576858410NoElective AdmissionNoYesMediumNoYesNoNoNoNoNoYesYesBlood Work1.7148792127.83042312993.43735035534555
45C5445235885f56b-d6da-43a3-8760-83583af94266d2f0425877b10ed6bb381f3e2579424aWest PointVAKing William2318137.59894-76.889585287RuralAmerica/New_YorkHealth promotion specialist1221209.56WidowedFemaleNo17.439069502YesElective AdmissionNoNoLowNoNoNoYesNoNoYesNoNoCT Scan1.2548072113.0732743716.52578621335343
56S543885e3b0a319-9e2e-4a23-8752-2fdc736c30f403e447146d4a32e1aaf75727c3d1230cBraggsOKMuskogee7442335.67302-95.19180981UrbanAmerica/ChicagoCorporate treasurer37681999.88Never MarriedMaleNo19.612646600NoObservation AdmissionNoNoMediumYesYesYesNoYesNoYesNoNoBlood Work5.9572502636.69118012742.58991045443546
67E5433022fccb53e-bd9a-4eaa-a53c-9dfc0cb83f94e4884a42ba809df6a89ded6c97f460d4ThompsonOHGeauga4408641.67511-81.057882558RuralAmerica/New_YorkHydrologist05010456.05Never MarriedMaleNo14.751687600NoEmergency AdmissionYesNoLowYesYesYesYesYesYesNoYesNoIntravenous9.0582103694.62716116815.51360043323455
78K477307ab634508-dd8c-42e5-a4e4-d101a46f24315f78b8699d1aa9b950b562073f629ca2StrasburgVAShenandoah2264139.08062-78.39150479UrbanAmerica/New_YorkPsychiatric nurse74038319.29DivorcedFemaleNo19.688673720NoObservation AdmissionNoNoMediumYesNoNoNoNoNoNoNoNoIntravenous14.2280193021.4990396930.57213812254242
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99909991M073419b73f4cb-3945-41c1-9a38-129fcecde3a04f83c32e349fa29482f338ed25896f01CrosbyMSWilkinson3963331.29102-91.184931236RuralAmerica/ChicagoBuyer, industrial06959914.82Never MarriedFemaleYes15.90575402NoElective AdmissionNoNoMediumYesNoNoYesNoNoNoYesNoCT Scan62.735997149.80811365.37034444433
99919992L715446a5492e46-bf07-4c9e-bd00-e96adba46557441b4934c2fdb97ce81fe317b4150a32BluntSDHughes5752244.47735-99.99679552UrbanAmerica/ChicagoTelecommunications researcher06144740.81SeparatedMaleYes19.25381320NoElective AdmissionNoYesMediumYesNoNoYesNoYesYesNoYesCT Scan56.874196756.52010263.56032343343
99929993T5235888dfe0df1-bf7b-48d0-83e9-0bc22d86d1681a35db97b8b90ab318b90b708904d312ColumbusOHFranklin4320339.97310-82.968988368RuralAmerica/New_YorkTraining and development officer35021989.44MarriedFemaleYes15.73466320YesEmergency AdmissionYesNoHighNoYesYesYesNoNoYesYesYesBlood Work56.615717894.20117367.97033424334
99939994Q117805ccc85472-5bd1-4389-8442-122a876b90009612abd4b9a81c2fd596ec9adb232efaNorthvaleNJBergen764741.00669-73.942595412UrbanAmerica/New_YorkLegal executive18315654.69MarriedMaleNo18.84074411NoEmergency AdmissionYesNoHighYesNoNoYesNoNoNoNoNoBlood Work40.355306294.44327882.08066533555
99949995M58349115c2b4bb-2c36-41b2-b1e2-206144fae1dcb9dd180aa8894ecea6af33a46b22e015FellsmereFLIndian River3294827.88942-80.733477908UrbanAmerica/New_YorkTechnical author66939797.05SeparatedMaleNo16.60460402NoEmergency AdmissionNoNoMediumNoYesNoNoNoNoNoYesYesBlood Work37.932125607.71612045.86023215342
99959996B863060a25b594d-0328-486f-a9b9-0567eb0f972339184dc28cc038871912ccc4500049e5NorlinaNCWarren2756336.42886-78.237164762UrbanAmerica/New_YorkProgrammer, multimedia22545967.61WidowedMaleNo16.98086421NoEmergency AdmissionYesNoMediumNoNoNoNoNoYesNoYesNoIntravenous51.561226850.9428927.64232234342
99969997P71204070711574-f7b1-4a17-b15f-48c54564b70f3cd124ccd43147404292e883bf9ec55cMilmayNJAtlantic834039.43609-74.873021251UrbanAmerica/New_YorkRestaurant manager, fast food48714983.02WidowedMaleYes18.17702500NoElective AdmissionYesNoMediumYesYesYesNoNoNoNoNoYesCT Scan68.668247741.69028507.15033425344
99979998R7788901d79569d-8e0f-4180-a207-d67ee4527d2641b770aeee97a5b9e7f69c906a8119d7SouthsideTNMontgomery3717136.36655-87.29988532RuralAmerica/ChicagoPsychologist, occupational34565917.81SeparatedFemaleYes17.12907420YesElective AdmissionYesNoHighYesNoNoNoNoYesYesNoNoIntravenous70.154188276.48115281.21033344232
99989999E344109f5a68e69-2a60-409b-a92f-ac0847b27db02bb491ef5b1beb1fed758cc6885c167aQuinnSDPennington5777544.10354-102.01590271RuralAmerica/DenverOutdoor activities/education manager34329702.32DivorcedMaleYes19.91043521NoEmergency AdmissionNoNoMediumYesNoNoNoYesNoNoNoNoBlood Work63.356907644.4837781.67855344343
999910000I569847bc482c02-f8c9-4423-99de-3db5e62a18d595663a202338000abdf7e09311c2a8a1CoraopolisPAAllegheny1510840.49998-80.1995941524UrbanAmerica/New_YorkSports development officer87062682.63SeparatedFemaleYes18.38862501NoObservation AdmissionNoNoLowYesYesNoYesNoNoYesNoNoBlood Work70.850597887.55311643.19043323643